1. Field of the Invention
The present invention relates to a coefficient learning apparatus and method, an image processing apparatus and method, a program, and a recording medium. More particularly, the present invention relates to a coefficient learning apparatus and method, an image processing apparatus and method, a program, and a recording medium that are capable of performing a high-quality image forming process with highly versatile characteristics more efficiently and at higher speed.
2. Description of the Related Art
In predicting a teacher image from a student (input) image containing deterioration, processing thereof by using one model in which the entire image is represented by a linear sum of student (input) images has a problem in accuracy. For this reason, a method is performed in which student (input) images are classified in accordance with a local feature amount, and a regression coefficient is switched for each class. Hitherto, methods that use 1-bit ADRC or a K-means algorithm for classification have been proposed.
For example, in order to convert a standard television signal (SD signal) into a high-resolution signal (HD signal), a technique using a classification adaptive process has been proposed (see, for example, Japanese Unexamined Patent Application Publication No. 7-79418).
In a case where an SD signal is converted into an HD signal by using the technology of Japanese Unexamined Patent Application Publication No. 7-79418, first, the feature of a class tap formed from an input SD signal is determined using ADRC (adaptive dynamic range coding) or the like, and classification is performed on the basis of the feature of the obtained class tap. Then, by performing computation between a prediction coefficient provided for each class and a prediction tap formed from the input SD signal, an HD signal is obtained.
Classification is designed such that high S/N pixels are grouped on the basis of a pattern of pixel values of low S/N pixels, which are at positions close in terms of space or time to the positions of the low S/N image, which correspond to the positions of the high S/N pixels, for which a prediction value is determined. The adaptive process is such that a prediction coefficient more appropriate for high S/N pixels belonging to a group is determined for each group (corresponding to the above-described class), and the image quality is improved on the basis of the prediction coefficient. Therefore, it is preferable that classification be performed in such a manner that, basically, class taps are formed using many more pixels, which are related to high S/N pixels for which a prediction value is determined.